Overview

Dataset statistics

Number of variables15
Number of observations14116
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory120.0 B

Variable types

Numeric8
Categorical6
Boolean1

Alerts

df_index is highly correlated with employee_idHigh correlation
employee_id is highly correlated with df_indexHigh correlation
age is highly correlated with n_projectsHigh correlation
n_projects is highly correlated with ageHigh correlation
df_index is highly correlated with employee_idHigh correlation
employee_id is highly correlated with df_indexHigh correlation
age is highly correlated with n_projectsHigh correlation
n_projects is highly correlated with ageHigh correlation
df_index is highly correlated with employee_id and 1 other fieldsHigh correlation
employee_id is highly correlated with df_index and 1 other fieldsHigh correlation
age is highly correlated with recently_promotedHigh correlation
avg_monthly_hrs is highly correlated with recently_promotedHigh correlation
last_evaluation is highly correlated with recently_promotedHigh correlation
n_projects is highly correlated with recently_promotedHigh correlation
recently_promoted is highly correlated with df_index and 5 other fieldsHigh correlation
df_index is highly correlated with employee_idHigh correlation
employee_id is highly correlated with df_index and 1 other fieldsHigh correlation
age is highly correlated with marital_status and 1 other fieldsHigh correlation
marital_status is highly correlated with age and 1 other fieldsHigh correlation
avg_monthly_hrs is highly correlated with n_projects and 2 other fieldsHigh correlation
n_projects is highly correlated with age and 4 other fieldsHigh correlation
satisfaction is highly correlated with avg_monthly_hrs and 2 other fieldsHigh correlation
status is highly correlated with employee_id and 3 other fieldsHigh correlation
df_index is uniformly distributed Uniform
df_index has unique values Unique
employee_id has unique values Unique

Reproduction

Analysis started2022-04-30 14:31:06.843626
Analysis finished2022-04-30 14:31:50.882087
Duration44.04 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct14116
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7071.104704
Minimum0
Maximum14144
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size110.4 KiB
2022-04-30T20:01:51.128438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile705.75
Q13532.75
median7070.5
Q310610.25
95-th percentile13438.25
Maximum14144
Range14144
Interquartile range (IQR)7077.5

Descriptive statistics

Standard deviation4084.958507
Coefficient of variation (CV)0.5776973582
Kurtosis-1.200746217
Mean7071.104704
Median Absolute Deviation (MAD)3539
Skewness0.0003525578936
Sum99815714
Variance16686886
MonotonicityStrictly increasing
2022-04-30T20:01:51.500344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
94331
 
< 0.1%
94221
 
< 0.1%
94231
 
< 0.1%
94241
 
< 0.1%
94251
 
< 0.1%
94261
 
< 0.1%
94271
 
< 0.1%
94281
 
< 0.1%
94291
 
< 0.1%
Other values (14106)14106
99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
141441
< 0.1%
141431
< 0.1%
141421
< 0.1%
141411
< 0.1%
141401
< 0.1%
141391
< 0.1%
141381
< 0.1%
141371
< 0.1%
141361
< 0.1%
141351
< 0.1%

employee_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct14116
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112120.6578
Minimum100101
Maximum148988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size110.4 KiB
2022-04-30T20:01:51.857568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum100101
5-th percentile101224.75
Q1105773.5
median111293.5
Q3116655.25
95-th percentile128001
Maximum148988
Range48887
Interquartile range (IQR)10881.75

Descriptive statistics

Standard deviation8497.639403
Coefficient of variation (CV)0.07579013156
Kurtosis2.759148176
Mean112120.6578
Median Absolute Deviation (MAD)5445.5
Skewness1.304670041
Sum1582695205
Variance72209875.42
MonotonicityStrictly increasing
2022-04-30T20:01:52.436635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001011
 
< 0.1%
1148631
 
< 0.1%
1148461
 
< 0.1%
1148471
 
< 0.1%
1148491
 
< 0.1%
1148511
 
< 0.1%
1148521
 
< 0.1%
1148531
 
< 0.1%
1148551
 
< 0.1%
1148561
 
< 0.1%
Other values (14106)14106
99.9%
ValueCountFrequency (%)
1001011
< 0.1%
1001021
< 0.1%
1001031
< 0.1%
1001051
< 0.1%
1001061
< 0.1%
1001071
< 0.1%
1001081
< 0.1%
1001091
< 0.1%
1001101
< 0.1%
1001111
< 0.1%
ValueCountFrequency (%)
1489881
< 0.1%
1489471
< 0.1%
1489161
< 0.1%
1488791
< 0.1%
1488771
< 0.1%
1488421
< 0.1%
1487681
< 0.1%
1487371
< 0.1%
1487191
< 0.1%
1486401
< 0.1%

age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct36
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.89600453
Minimum22
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size110.4 KiB
2022-04-30T20:01:52.800829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile22
Q124
median29
Q341
95-th percentile52
Maximum57
Range35
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.975000045
Coefficient of variation (CV)0.3032283156
Kurtosis-0.867621121
Mean32.89600453
Median Absolute Deviation (MAD)6
Skewness0.7008830699
Sum464360
Variance99.50062591
MonotonicityNot monotonic
2022-04-30T20:01:53.314800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
241308
 
9.3%
251246
 
8.8%
231196
 
8.5%
221166
 
8.3%
27662
 
4.7%
29660
 
4.7%
28647
 
4.6%
26626
 
4.4%
42303
 
2.1%
37284
 
2.0%
Other values (26)6018
42.6%
ValueCountFrequency (%)
221166
8.3%
231196
8.5%
241308
9.3%
251246
8.8%
26626
4.4%
27662
4.7%
28647
4.6%
29660
4.7%
30275
 
1.9%
31225
 
1.6%
ValueCountFrequency (%)
5734
 
0.2%
5622
 
0.2%
5538
 
0.3%
54226
1.6%
53235
1.7%
52252
1.8%
51227
1.6%
50233
1.7%
49243
1.7%
48272
1.9%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size110.4 KiB
Male
9287 
Female
4829 

Length

Max length6
Median length4
Mean length4.684188155
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male9287
65.8%
Female4829
34.2%

Length

2022-04-30T20:01:53.759462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T20:01:54.023402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
male9287
65.8%
female4829
34.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

marital_status
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size110.4 KiB
Unmarried
7211 
Married
6905 

Length

Max length9
Median length9
Mean length8.021677529
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnmarried
2nd rowUnmarried
3rd rowUnmarried
4th rowUnmarried
5th rowUnmarried

Common Values

ValueCountFrequency (%)
Unmarried7211
51.1%
Married6905
48.9%

Length

2022-04-30T20:01:54.269978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T20:01:54.468549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
unmarried7211
51.1%
married6905
48.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

avg_monthly_hrs
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct249
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.9926325
Minimum49
Maximum310
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size110.4 KiB
2022-04-30T20:01:54.716402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile128
Q1155
median199
Q3245
95-th percentile275
Maximum310
Range261
Interquartile range (IQR)90

Descriptive statistics

Standard deviation50.82695196
Coefficient of variation (CV)0.2541441219
Kurtosis-1.044324553
Mean199.9926325
Median Absolute Deviation (MAD)45
Skewness0.01643063443
Sum2823096
Variance2583.379046
MonotonicityNot monotonic
2022-04-30T20:01:55.089798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135143
 
1.0%
156141
 
1.0%
151140
 
1.0%
149139
 
1.0%
145125
 
0.9%
143124
 
0.9%
160123
 
0.9%
260118
 
0.8%
154118
 
0.8%
148118
 
0.8%
Other values (239)12827
90.9%
ValueCountFrequency (%)
493
< 0.1%
521
 
< 0.1%
542
< 0.1%
551
 
< 0.1%
561
 
< 0.1%
601
 
< 0.1%
631
 
< 0.1%
651
 
< 0.1%
662
< 0.1%
674
< 0.1%
ValueCountFrequency (%)
31018
0.1%
30915
0.1%
30819
0.1%
30714
0.1%
30617
0.1%
30518
0.1%
30417
0.1%
3036
 
< 0.1%
3028
 
0.1%
30121
0.1%

department
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size110.4 KiB
D00-SS
4601 
D00-ENG
2573 
D00-SP
2108 
D00D00D00D00-IT
1152 
D00-PD
853 
Other values (7)
2829 

Length

Max length15
Median length6
Mean length7.004746387
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD00-SS
2nd rowD00-MN
3rd rowD00-ENG
4th rowD00-MT
5th rowD00-ENG

Common Values

ValueCountFrequency (%)
D00-SS4601
32.6%
D00-ENG2573
18.2%
D00-SP2108
14.9%
D00D00D00D00-IT1152
 
8.2%
D00-PD853
 
6.0%
D00-MT812
 
5.8%
D00-FN722
 
5.1%
D00-MN590
 
4.2%
D00D00D00-IT207
 
1.5%
D00-AD175
 
1.2%
Other values (2)323
 
2.3%

Length

2022-04-30T20:01:55.447323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
d00-ss4601
32.6%
d00-eng2573
18.2%
d00-sp2108
14.9%
d00d00d00d00-it1152
 
8.2%
d00-pd853
 
6.0%
d00-mt812
 
5.8%
d00-fn722
 
5.1%
d00-mn590
 
4.2%
d00d00d00-it207
 
1.5%
d00-ad175
 
1.2%
Other values (2)323
 
2.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size110.4 KiB
0
12075 
1.00
2041 
ValueCountFrequency (%)
012075
85.5%
1.002041
 
14.5%
2022-04-30T20:01:55.621914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

last_evaluation
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12185
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7183221625
Minimum0.316175
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size110.4 KiB
2022-04-30T20:01:55.836215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.316175
5-th percentile0.45900175
Q10.5795165
median0.7183221625
Q30.85685375
95-th percentile0.976118
Maximum1
Range0.683825
Interquartile range (IQR)0.27733725

Descriptive statistics

Standard deviation0.1636994965
Coefficient of variation (CV)0.2278914741
Kurtosis-0.9861755436
Mean0.7183221625
Median Absolute Deviation (MAD)0.138693
Skewness-0.06847563778
Sum10139.83565
Variance0.02679752515
MonotonicityNot monotonic
2022-04-30T20:01:56.357559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.71832216251487
 
10.5%
1356
 
2.5%
0.8962463
 
< 0.1%
0.666312
 
< 0.1%
0.8386462
 
< 0.1%
0.9748142
 
< 0.1%
0.8899852
 
< 0.1%
0.7448342
 
< 0.1%
0.9555792
 
< 0.1%
0.5741662
 
< 0.1%
Other values (12175)12256
86.8%
ValueCountFrequency (%)
0.3161751
< 0.1%
0.3172791
< 0.1%
0.3209531
< 0.1%
0.3228281
< 0.1%
0.3242391
< 0.1%
0.3258851
< 0.1%
0.3284171
< 0.1%
0.3298131
< 0.1%
0.331321
< 0.1%
0.3315451
< 0.1%
ValueCountFrequency (%)
1356
2.5%
0.9998081
 
< 0.1%
0.999391
 
< 0.1%
0.9993651
 
< 0.1%
0.9992591
 
< 0.1%
0.999211
 
< 0.1%
0.999151
 
< 0.1%
0.9991451
 
< 0.1%
0.9991131
 
< 0.1%
0.9990621
 
< 0.1%

n_projects
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.777769906
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size110.4 KiB
2022-04-30T20:01:56.723503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.249693245
Coefficient of variation (CV)0.3308018422
Kurtosis-0.4814501348
Mean3.777769906
Median Absolute Deviation (MAD)1
Skewness0.3152883573
Sum53327
Variance1.561733206
MonotonicityNot monotonic
2022-04-30T20:01:56.984976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
44044
28.6%
33788
26.8%
52566
18.2%
22322
16.4%
61093
 
7.7%
7242
 
1.7%
161
 
0.4%
ValueCountFrequency (%)
161
 
0.4%
22322
16.4%
33788
26.8%
44044
28.6%
52566
18.2%
61093
 
7.7%
7242
 
1.7%
ValueCountFrequency (%)
7242
 
1.7%
61093
 
7.7%
52566
18.2%
44044
28.6%
33788
26.8%
22322
16.4%
161
 
0.4%

recently_promoted
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size110.4 KiB
0.0
13819 
1.0
 
297

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.013819
97.9%
1.0297
 
2.1%

Length

2022-04-30T20:01:57.273166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T20:01:57.423866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.013819
97.9%
1.0297
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

salary
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size110.4 KiB
low
6889 
medium
6086 
high
1141 

Length

Max length6
Median length4
Mean length4.374256163
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlow
2nd rowhigh
3rd rowlow
4th rowlow
5th rowlow

Common Values

ValueCountFrequency (%)
low6889
48.8%
medium6086
43.1%
high1141
 
8.1%

Length

2022-04-30T20:01:57.941144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T20:01:58.181247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
low6889
48.8%
medium6086
43.1%
high1141
 
8.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

satisfaction
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13493
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6216535485
Minimum0.0400584
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size110.4 KiB
2022-04-30T20:01:58.388013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.0400584
5-th percentile0.13733525
Q10.452826
median0.6525485
Q30.82296025
95-th percentile0.969316
Maximum1
Range0.9599416
Interquartile range (IQR)0.37013425

Descriptive statistics

Standard deviation0.2491466421
Coefficient of variation (CV)0.4007805355
Kurtosis-0.6424061277
Mean0.6216535485
Median Absolute Deviation (MAD)0.1837245
Skewness-0.481363745
Sum8775.261491
Variance0.06207404925
MonotonicityNot monotonic
2022-04-30T20:01:58.938924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1356
 
2.5%
0.6525485150
 
1.1%
0.8828922
 
< 0.1%
0.4143752
 
< 0.1%
0.5709542
 
< 0.1%
0.6970192
 
< 0.1%
0.9224572
 
< 0.1%
0.4976122
 
< 0.1%
0.4709552
 
< 0.1%
0.5651652
 
< 0.1%
Other values (13483)13594
96.3%
ValueCountFrequency (%)
0.04005841
< 0.1%
0.04019081
< 0.1%
0.04047741
< 0.1%
0.04130171
< 0.1%
0.04240751
< 0.1%
0.04484411
< 0.1%
0.04558071
< 0.1%
0.04609361
< 0.1%
0.04945921
< 0.1%
0.04954881
< 0.1%
ValueCountFrequency (%)
1356
2.5%
0.999881
 
< 0.1%
0.9997631
 
< 0.1%
0.9997041
 
< 0.1%
0.9995931
 
< 0.1%
0.9995861
 
< 0.1%
0.9995561
 
< 0.1%
0.9995121
 
< 0.1%
0.9994391
 
< 0.1%
0.9993551
 
< 0.1%

tenure
Real number (ℝ≥0)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.492419949
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size110.4 KiB
2022-04-30T20:01:59.424616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median3
Q34
95-th percentile6
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.453547798
Coefficient of variation (CV)0.4162007487
Kurtosis4.857579404
Mean3.492419949
Median Absolute Deviation (MAD)1
Skewness1.871919281
Sum49299
Variance2.1128012
MonotonicityNot monotonic
2022-04-30T20:01:59.725338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
36156
43.6%
23019
21.4%
42386
 
16.9%
51363
 
9.7%
6659
 
4.7%
10198
 
1.4%
7180
 
1.3%
8155
 
1.1%
ValueCountFrequency (%)
23019
21.4%
36156
43.6%
42386
 
16.9%
51363
 
9.7%
6659
 
4.7%
7180
 
1.3%
8155
 
1.1%
10198
 
1.4%
ValueCountFrequency (%)
10198
 
1.4%
8155
 
1.1%
7180
 
1.3%
6659
 
4.7%
51363
 
9.7%
42386
 
16.9%
36156
43.6%
23019
21.4%

status
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size110.4 KiB
Employed
10761 
Left
3355 

Length

Max length8
Median length8
Mean length7.049305752
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEmployed
2nd rowEmployed
3rd rowEmployed
4th rowEmployed
5th rowLeft

Common Values

ValueCountFrequency (%)
Employed10761
76.2%
Left3355
 
23.8%

Length

2022-04-30T20:01:59.965942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T20:02:00.163375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
employed10761
76.2%
left3355
 
23.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-04-30T20:01:46.500327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:24.893376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:28.413508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:31.848229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:34.666524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:37.455746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:40.433625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:43.352252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:46.836280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:25.294312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:28.900955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:32.166692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:35.033726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:37.738640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:40.767280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:43.832488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:47.157614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:25.698398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:29.387866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:32.399617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:35.385378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:38.195965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:41.078655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:44.153282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:47.423752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:26.033221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:29.777030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:32.705307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:35.632205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:38.737273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:41.370286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:44.421487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:47.686095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:26.573292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:30.220808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:32.912114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:35.926332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:39.067238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:41.665924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:44.852967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:48.100369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:27.049224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:30.564084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:33.208753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:36.432344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:39.417393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:42.085940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:45.195227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:48.463151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:27.532645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:31.015252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:33.479171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:36.777945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:39.750610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:42.533238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:45.517380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:48.761928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:27.862668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:31.512845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:33.782586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:37.228940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:40.077890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:42.849052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-30T20:01:45.842068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-30T20:02:00.312060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-30T20:02:00.818639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-30T20:02:01.372334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-30T20:02:01.904224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-30T20:02:02.505941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-30T20:01:49.658890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-30T20:01:50.577130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexemployee_idagegendermarital_statusavg_monthly_hrsdepartmentfiled_complaintlast_evaluationn_projectsrecently_promotedsalarysatisfactiontenurestatus
0010010126MaleUnmarried156.0D00-SS00.59910920.0low0.5651002.0Employed
1110010225FemaleUnmarried172.0D00-MN00.75420031.0high0.48622010.0Employed
2210010324MaleUnmarried268.0D00-ENG1.000.68236630.0low0.6125252.0Employed
3310010523FemaleUnmarried192.0D00-MT1.000.75971130.0low0.6156413.0Employed
4410010629MaleUnmarried145.0D00-ENG00.51711020.0low0.5176843.0Left
5510010752MaleMarried178.0D00-ENG00.50098860.0low0.3652912.0Employed
6610010824MaleUnmarried184.0D00-ENG1.000.81547730.0medium0.9243653.0Employed
7710010924MaleUnmarried177.0D00-MT00.46148930.0high0.3501683.0Employed
8810011025MaleUnmarried235.0D00-ENG00.94639930.0medium0.6087872.0Employed
9910011122MaleUnmarried138.0D00-ENG00.48928620.0low0.3697783.0Left

Last rows

df_indexemployee_idagegendermarital_statusavg_monthly_hrsdepartmentfiled_complaintlast_evaluationn_projectsrecently_promotedsalarysatisfactiontenurestatus
141061413514864033FemaleMarried218.0D00-PD00.53623030.0low0.7545143.0Employed
141071413614871928MaleUnmarried177.0D00-ENG01.00000030.0medium0.8125092.0Employed
141081413714873731FemaleMarried162.0D00-PR00.56572530.0low0.8512342.0Employed
141091413814876843MaleMarried232.0D00-FN1.000.92720350.0medium0.9029795.0Left
141101413914884242MaleMarried232.0D00-SS00.43691350.0medium0.6646344.0Employed
141111414014887725MaleUnmarried136.0D00-SS00.69296330.0high0.7928142.0Employed
141121414114887926MaleUnmarried217.0D00-MT00.71832230.0high0.7657573.0Employed
141131414214891623FemaleUnmarried171.0D00-ENG00.71832220.0low0.5838522.0Employed
141141414314894728MaleUnmarried221.0D00-SP00.84032530.0medium0.7951882.0Employed
141151414414898822MaleUnmarried130.0D00-SP00.51389120.0medium0.4066453.0Left